Data
Melbourne_Housing_Snapshot

Melbourne_Housing_Snapshot

active ARFF Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) Visibility: public Uploaded 31-05-2024 by Iwo Godzwon
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### Description: The dataset, named `melb_data.csv`, represents detailed information about real estate sales in various suburbs across Melbourne. It captures specific characteristics of residential properties along with sales information, providing a comprehensive snapshot for potential buyers, sellers, and analysts. ### Attribute Description: - Suburb: Name of the suburb where the property is located. - Address: Specific address of the property. - Rooms: Number of rooms in the property. - Type: Type of dwelling (h: house, u: unit/duplex). - Price: Sale price of the property in Australian dollars. - Method: Sale method (S: Sold). - SellerG: Real estate agency or agent selling the property. - Date: Date of sale. - Distance: Distance from the property to the Central Business District (CBD) in kilometers. - Postcode: The postal code of the property location. - Bedroom2: Number of bedrooms (alternative count to Rooms). - Bathroom: Number of bathrooms. - Car: Number of parking spaces. - Landsize: Size of the land on which the property is situated in square meters. - BuildingArea: Size of the building in square meters; some values are missing (noted as 'nan'). - YearBuilt: The year the building was constructed; some dates are missing (noted as 'nan'). - CouncilArea: Governed council area for the property. - Lattitude: Geographic coordinate specifying the north-south position. - Longtitude: Geographic coordinate specifying the east-west position. - Regionname: General region (metropolitan region) where the property is located. - Propertycount: Number of properties in the suburb. ### Use Case: This dataset is predominantly geared towards property investors, real estate analysts, and market researchers who aim to understand the dynamics of Melbourne's real estate market. It can be used for various analyses, such as identifying property price trends, understanding the impact of location on property prices, and examining the features that contribute to the valuation of residential real estate. Additionally, the dataset is valuable for potential homebuyers seeking insights into property attributes and pricing within different Melbourne suburbs.

21 features

Suburbnominal314 unique values
0 missing
Addressstring13378 unique values
0 missing
Roomsnumeric9 unique values
0 missing
Typenominal3 unique values
0 missing
Pricenumeric2204 unique values
0 missing
Methodnominal5 unique values
0 missing
SellerGnominal268 unique values
0 missing
Datestring58 unique values
0 missing
Distancenumeric202 unique values
0 missing
Postcodenominal198 unique values
0 missing
Bedroom2numeric12 unique values
0 missing
Bathroomnominal9 unique values
0 missing
Carnominal11 unique values
62 missing
Landsizenumeric1448 unique values
0 missing
BuildingAreanumeric602 unique values
6450 missing
YearBuiltnominal144 unique values
5375 missing
CouncilAreanominal33 unique values
1369 missing
Lattitudenumeric6503 unique values
0 missing
Longtitudenumeric7063 unique values
0 missing
Regionnamenominal8 unique values
0 missing
Propertycountnumeric311 unique values
0 missing

19 properties

13580
Number of instances (rows) of the dataset.
21
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
13256
Number of missing values in the dataset.
7384
Number of instances with at least one value missing.
9
Number of numeric attributes.
10
Number of nominal attributes.
0
Percentage of binary attributes.
54.37
Percentage of instances having missing values.
Average class difference between consecutive instances.
4.65
Percentage of missing values.
0
Number of attributes divided by the number of instances.
42.86
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
47.62
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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